Embodiments of the present invention relate generally to digital image processing and more particularly to image processing techniques that employ computer vision for optimizing object recognition.
Object recognition techniques are widely applied in medical image analysis, industrial machine vision systems, security and authentication applications, biometric systems, and so on, for recognizing objects using acquired image data. Particularly, object recognition is used in present day biometric applications for protecting electronically stored information and for identifying and authenticating individuals. Face recognition, being a non-contact biometric technique, provides a competitive and convenient technique for identifying individuals. A core challenge for face recognition techniques, however, is to derive a feature representation of facial images that facilitates accurate identification. To that end, conventional subspace training face recognition algorithms are employed to provide better face recognition performance. These algorithms project facial images into lower dimension subspaces that preserve intrinsic properties of the acquired image data, thereby enhancing face recognition performance scores.
Some of the subspace training algorithms, such as Principal Component Analysis (PCA) and Independent Component Analysis (ICA) focus on finding subspaces that preserve distributive properties of acquired image data. Other subspace training algorithms, such as Fisher Discriminant Analysis (FDA), Locality Preserving Projections (LPP), and Marginal Fisher Analysis (MFA) preserve the discriminative or locality properties of the acquired image data. Generally, the discriminative or locality properties align well with the face recognition performance scores. These properties, however, may lead to suboptimal subspaces when the image data does not satisfy one or more assumptions such as those concerning intrapersonal variations and imaging conditions. In a real-world application, such as in an ID kiosk face verification application, a query image and a set of reference images are acquired in different settings. Statistical incoherence between the query image and the set of reference images on account of different imaging conditions considerably affects subspace determination capability, thus resulting in inefficient face recognition performance.
Furthermore, many face recognition techniques employ a large number of features to enhance the face recognition performance. Employing such a large number of features along with a determined amount of training data, however, considerably increases the computational burden on a training system, thereby affecting face recognition results. Consequently, conventional subspace training algorithms employ fewer features, thereby imposing limitations on further improvements to the face recognition performance.
It may therefore be desirable to develop efficient techniques that improve the face recognition performance by determining optimal subspaces. Further, there is a need for techniques that provide efficient computations that result in better recognition performance even with a limited number of features or training data.